Developments in microfluidic technology and micro-total analytical systems (microTAS) have proceeded rapidly over the past two decades (Auroux, et al. 2002, Analytical Chemistry 74(12): 2637-2652; Reyes, et al. 2002, Analytical Chemistry 74(12); 2623-2636; Dittrich, et al. 2006, Analytical Chemistry 78(12); 3887-3908). Microfluidic technology promises to have major and far-reaching impact on analytical testing, environmental monitoring, biodefense, and health care. One area that is receiving special focus by many researchers and investors is the development of microfluidic-based point-of-care diagnostic systems (Yager, et al. 2006, Nature 442(7101); 412-418). Due to small sample and reagent requirements, laminar fluid flow, and speed, microfluidic devices can drastically reduce the cost, inconvenience, and time required to analyze a patient sample.
Many researchers who publish for the microfluidic and point-of-care diagnostics literature seem to choose relatively simple assay designs designed solely to demonstrate the function of a novel device they have constructed. These assays are often demonstrated using model systems, meaning the assays are conducted in very simple matrices (such as defined buffer solutions that contain no interferents). Rarely are real patient samples used that have independently been verified to contain the concentration of analyte measured by the new device. A detailed literature is available that describes the processes that govern the outcome of these common assay methods, and it has shown that the physical and chemical processes that underlie these methods are, in fact, anything but simple (Lionello, et al. 2005, Lab on a Chip 5: 254-260, and 1096-1103; Zimmermann, et al. 2005, Biomedical Microdevices 7(2): 99-110, Gervias, et al. 2006, Lab on a Chip 6: 500-507; Gervias and Jensen 2006, Chemical Engineering Science 61: 1102-1121).
The vast majority of biosensors in use or under development rely on the binding of a molecule to an activated surface, and many provide data on the kinetics of binding that are interpreted to obtain quantitative information, such as the concentration of the binding (or competing) species of interest present in the original sample. Until recently, most biosensors have used single-point or spectroscopic detectors (i.e., sensors that produce scalar or vector data, also referred to as zeroth-order or first-order data, respectively). Developments in analytical instrumentation, particularly those that focus on the ability to image biosensor surfaces, have opened up whole new dimensions of potential assay data (literally, simply by adding in an orthogonal spatial index). Therefore, these analytical instruments present researchers and clinicians with powerful new opportunities to obtain subtle analytical information, such as simultaneous multi-species detection, background correction, and run-time calibration, and to do so within minutes rather than the hours typically required for presently used methods. The development of microfluidic assays that exploit these additional dimensions to provide additional quantitative data, not to mention the theories necessary to take advantage of this data, is in its infancy.
Regardless of the format of the assay, in order to make quantitative measurements that represent the true value of the analyte(s) in an unknown, it is essential that the volumes, concentrations and times of interaction of chemical species in the assay system be known to high precision. In contrast to traditional formats, such as those that use a 96-well plate in which the reacting species are provided with lengthy time periods to interact, and that typically provide scalar measurements regarding assay outcomes (e.g., OD), microfluidic assays are often conducted far from equilibrium end-points and can be highly dependent on the small-scale differences in solute concentrations and fluid flow rates, both in space and/or time (Foley, et al. 2007, Analytical Chemistry, 79(10): 3549-3553; Nelson, et al. 2007, Analytical Chemistry, 79(10): 3542-3548).
It is well known that solutes in dissimilar fluids disperse amongst the fluids under the influence of differential velocity fields (such as in fluids in ducts experiencing pressure-driven flow), which leads to solute concentration gradients in the fluids that vary with space and time. Analytical solutions to the concentration of solutes moving in dissimilar fluids under laminar, pressure driven flow were reported by Taylor and Aris in the 1950s (Taylor 1953, Proc. Royal Soc. London. Series A, Mathematical and Physical Sciences 219(1137): 186-203; Taylor 1954, Proc. Royal Soc. London. Series A, Mathematical and Physical Sciences 225(1163): 473-477; Aris 1956, Proc. Royal Soc. London. Series A, Mathematical and Physical Sciences 235(1200): 67-77, Aris 1959, Proc. Royal Soc. London. Series A, Mathematical and Physical Sciences 252(1271): 538-550). However, absent the development of resource-intensive computational models to predict the dispersion behavior of arbitrary channel geometries, it is difficult (if not impossible) to predict the dispersion profile of a given device. This is particularly true when that device is susceptible to random errors during use, such as variations in device geometry due to errors in manufacturing, the presence or appearance of bubbles, and the like. Nevertheless, the dispersion characteristics of a device may have a strong influence on the outcome of a flow-based assay, since the concentration of species near a biosensor surface will be determined not only by the concentration of analyte in the original sample but on the diluting and redistributing effects of dispersion, particularly at early times after introduction of sample or reagent, which occurs when the assay outcome is measured as rapidly as possible, almost always far from thermodynamic equilibrium. Therefore, in order to accurately correlate a given sensor signal to an analytical measurement, or preferably to take advantage of the dynamic yet reproducible processes that occur in microfluidic assays, it is vital to have detailed information regarding the spatiotemporal concentration and flow rate profiles of the fluids above the biosensor surface. To date, this information has been particularly difficult to obtain, often requiring complex or imprecise instrumentation. Either that, or these controlling processes have simply been neglected, possibly to the detriment of the ability to make valid, accurate, and reproducible measurements.
Ruzicka and Hansen both mention in their recent editorial publications their puzzlement that microTAS investigators seemingly largely neglected well-proven dispersion principles used in FIA in their analyses (e.g., (Ruzicka 2005, “Flow Injection Analysis”, (3rd ed.) Self-published CD-ROM)). It is noteworthy that Ruzicka and Hansen also recently argue in favor of larger fluidic cross-sections (i.e., diameters>1 mm) in the design their analytical instruments, and write that micro and nanofluidics may not find widespread application after all, due to potential failures due to obstructions and the requirement for high pressures to drive fluid flow through narrow channels (Ruzicka and Hansen 2000, Analytical Chemistry 72(5): 212A-217A; Hansen and Miro 2007, Trends in Analytical Chemistry 26(1): 18-26). On the other side of the FIA/microTAS coin, it is interesting to note that Manz et al. scarcely mention the use of FIA in recent reviews of the state of the art of MicroTAS technology (Auroux, et al. 2002, supra; Reyes, et al. 2002, supra; Dittrich, et al. 2006, supra). And yet it has been shown in many cases to be feasible to implement FIA using microfluidic devices (Leach, et al. 2003, Analytical Chemistry 75(4): 967-972). Moreover, recent perspectives suggest a fertile overlap between microfluidics and FIA (Smith and Hinson-Smith 2002, Analytical Chemistry 74(13): 385A-388A).
Ruzicka writes recently that the lack of broad adoption of FIA into microTAS may be because of the difficulty in machining high-precision valves required for high precision FIA experiments (Ruzicka and Hansen 2000, supra; Ruzicka 2005, supra). As of 2002, for example, commercially available FIA instruments have been priced at several tens of thousands of dollars (Smith and Hinson-Smith 2002, supra). Apparently, the current view about FIA instrumentation seems to be that they must provide highly precise timing and reproducible dispersion functions in order to utilize FIA principles. This level of precision is currently difficult to achieve using low-cost or disposable microfluidic devices, particularly those that utilize commonly available methods for flow control (such as micromachined valves, stepper motor controlled syringe pumps and valves, and as opposed to electrokinetic flow).
For reusable analytical devices, it is often possible to calibrate their operation in advance of measurement using known reference materials. However, it would be truly a leap forward toward the goal of rapid, point-of-care diagnostic testing to develop the ability to monitor and calibrate each disposable device individually and at run-time, in such a way as to correct for errors in solute concentration produced by dispersion. The device and method described herein describes a general method for doing so. While the example presented here uses a flat sensor with surface-sensitive detection, the methods could potentially be extended to other sensor geometries and detection methods.